Caracterización de la población de estudiantes mujeres en los programas de Ingeniería en Colombia: inscripción, admisión, matrícula y graduación
Bibliographic record
Abstract
The gender gap in Engineering is a relevant global issue.Evidence of this relevance is the dedication to a sustainable development objective, motivating the generation of global strategies to reduce it.In STEM programs, specifically, this gap is even more critical.One of the difficulties encountered when trying to address this issue of the gender gap in areas such as Engineering is the definition of the baseline and the proposal of basic exploratory studies to define strategies for the inclusion of women in science and engineering.Studies characterizing the evolution and current state of the population of female students in engineering programs in Colombia can be an interesting starting point to address gender gap issues in this region of the world.Therefore, the objective of this work is to generate a baseline of the population of female students in terms of inscription, admission, enrollment, and graduation in Engineering programs in Colombia for the period 2014-2022 based on the application of a methodology of descriptive and applied statistics and present an initial literature search to publicize the state of scientific production about strategies for the inclusion of women in science and engineering, from which, among other aspects, the marked increase in production was identified since 2020, with 2021 being the most active year and countries such as the US and Canada with the highest report, highlighting Brazil at the South American level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".